发表机构
Graduate School of Science and Technology, Shizuoka University; RIKEN AIP（静冈大学科学技术研究生院; 理化学研究所人工智能项目）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Face Re-morphing方法，利用再形变图像的特征空间相似度变化作为线索，在FRLL-Morph等数据集上实现了更优的差分形变攻击检测性能。
AI 中文摘要
人脸形变攻击对人脸识别系统构成严重威胁，因为单张形变文档图像可与多个贡献者匹配。差分形变攻击检测（D-MAD）通过将文档图像与可信活体图像进行比较来应对该威胁，但现有方法常依赖静态特征差异、组成人脸重建或多线索融合。本文提出Face Re-morphing，一种利用附加形变操作的特征空间响应作为检测线索的D-MAD方法。给定文档图像和可信活体图像，该方法生成再形变图像，并将文档-活体与活体-再形变之间的余弦相似度变化作为检测分数。在FRLL-Morph和FEI Morph上的实验表明，该线索在不同形变条件、再形变方法和人脸识别模型下均有效；与现有方法在AMSL上的对比显示出良好结果，且在FEI Morph版本1的Criminal条件下表现出色，尤其使用MorDIFF时。这些结果表明，再形变诱导的相似度变化为D-MAD提供了互补线索。
英文摘要
Face morphing attacks pose a serious threat to face recognition systems because a single morphed document image can be matched to multiple contributors. Differential morphing attack detection (D-MAD) addresses this threat by comparing a document image with a trusted live image, but existing methods often rely on static feature differences, constituent-face reconstruction, or multi-cue fusion. This paper proposes Face Re-morphing, a D-MAD method that uses the feature-space response to an additional morphing operation as a detection cue. Given a document image and a trusted live image, the proposed method generates a re-morphed image and uses the change between the document--live and live--re-morphed cosine similarities as the detection score. Experiments on FRLL-Morphs and FEI Morph show that the proposed cue is effective across different morphing conditions, re-morphing methods, and face recognition models. Comparisons with existing methods show favorable results on AMSL and indicate that the proposed method performs well under the Criminal condition on FEI Morph Version~1, particularly when using MorDIFF. These results indicate that re-morphing-induced similarity change provides a complementary cue for D-MAD.
Commentsaccepted to IJCB2026
